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Found 11 papers, 2 papers with code
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving.
P2M2-Net: Part-Aware Prompt-Guided Multimodal Point Cloud Completion
Inferring missing regions from severely occluded point clouds is highly challenging.
Opportunities for Adaptive Experiments to Enable Continuous Improvement that Trades-off Instructor and Researcher Incentives
In adaptive experiments, as different arms/conditions are deployed to students, data is analyzed and used to change the experience for future students.
A network-based biomarkers discovery of Cold/Hot ZHENG chronic gastritis and Cold/Hot herbs of formulae
And we collected 25 formulae (with traditional effects related to Cold/Hot ZHENG) for CG and corresponding 89 Cold/Hot herbs (including Warm/Cool herbs) to discover features and construct target networks of Cold/Hot herbs on the basis of network target and enrichment analysis.
Parameter efficient dendritic-tree neurons outperform perceptrons
Biological neurons are more powerful than artificial perceptrons, in part due to complex dendritic input computations.
Statistical Consequences of Dueling Bandits
Multi-Armed-Bandit frameworks have often been used by researchers to assess educational interventions, however, recent work has shown that it is more beneficial for a student to provide qualitative feedback through preference elicitation between different alternatives, making a dueling bandits framework more appropriate.
FCCDN: Feature Constraint Network for VHR Image Change Detection
Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning.
An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
Semantic segmentation is an essential part of deep learning.
Anomaly Detection by One Class Latent Regularized Networks
Anomaly detection is a fundamental problem in computer vision area with many real-world applications.
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks
Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the normal ones.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning
The land-use map is an important data that can reflect the use and transformation of human land, and can provide valuable reference for land-use planning.
